Join the conversation
![](https://codanics.com/wp-content/uploads/2024/04/IMG_16954467347071342.jpg)
Done
Reply
![](https://codanics.com/wp-content/uploads/2024/04/IMG_20240410_175137-1-scaled.jpg)
Done
Reply
![](https://codanics.com/wp-content/uploads/2023/10/e24e3cc0-f9cd-49cc-9e5e-e7fea619bd42.jpg)
I learned in this video Scaling and Normalization.
Reply
![](https://codanics.com/wp-content/uploads/2024/05/My_profile_pic.jpg)
I learned in this lecture about Scaling and Normalization.
Reply
![](https://codanics.com/wp-content/uploads/2023/10/9dd24f5a-b137-440a-baba-1855925152a0.jpg)
Feature scaling (standardization) transforms features to have a zero mean and unit variance. It is useful when the scale of the features needs to be consistent across all variables.Normalization scales features to a specific range, typically between 0 and 1. It is useful when the range of feature values needs to be controlled or when specific algorithms require input in a certain range.
The function of STANDARDIZATION is to bring the data to scale, and the function of NORMALIZATION is to have an impact on the distribution of the data.
Reply
![](https://codanics.com/wp-content/uploads/2024/02/2.jpg)
Scaling:- To change the range of data without changing the shape of the data.
Normalization:- Transform data into scale and change its distribution.
Reply